Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards

• URL: http://arxiv.org/abs/2106.12041v1
• Date: Fri, 11 Jun 2021 01:50:16 GMT
• Title: Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards
• Authors: Alana Hough and Tony E. Wong
• Abstract summary: We use random forests to examine the parametric drivers of future climate risk and how the relative importances of those drivers change over time. We find that the equilibrium climate sensitivity and a factor that scales the effect of aerosols on radiative forcing are consistently the most important climate model parametric uncertainties throughout the 2020 to 2150 interval for both low and high radiative forcing scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Climate models are critical tools for developing strategies to manage the risks posed by sea-level rise to coastal communities. While these models are necessary for understanding climate risks, there is a level of uncertainty inherent in each parameter in the models. This model parametric uncertainty leads to uncertainty in future climate risks. Consequently, there is a need to understand how those parameter uncertainties impact our assessment of future climate risks and the efficacy of strategies to manage them. Here, we use random forests to examine the parametric drivers of future climate risk and how the relative importances of those drivers change over time. We find that the equilibrium climate sensitivity and a factor that scales the effect of aerosols on radiative forcing are consistently the most important climate model parametric uncertainties throughout the 2020 to 2150 interval for both low and high radiative forcing scenarios. The near-term hazards of high-end sea-level rise are driven primarily by thermal expansion, while the longer-term hazards are associated with mass loss from the Antarctic and Greenland ice sheets. Our results highlight the practical importance of considering time-evolving parametric uncertainties when developing strategies to manage future climate risks.

Related papers

• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics [40.517778024431244]
  We introduce Extreme Model Output Statistics (X-MOS) to improve predictions of natural disaster risks. This approach utilizes deep regression techniques to precisely map CMIP model outputs to real measurements obtained from weather stations. In contrast to previous research, our study places a strong emphasis on enhancing the estimation of the tails of future climate parameter distributions.
  arXiv  Detail & Related papers  (2023-10-24T13:18:53Z)
• Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach [4.32583920500711]
  This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale.
  arXiv  Detail & Related papers  (2023-10-19T02:13:38Z)
• Climate-sensitive Urban Planning through Optimization of Tree Placements [55.11389516857789]
  Climate change is increasing the intensity and frequency of many extreme weather events, including heatwaves. Among the most promising strategies is harnessing the benefits of urban trees in shading and cooling pedestrian-level environments. Physical simulations can estimate the radiative and thermal impact of trees on human thermal comfort but induce high computational costs. We employ neural networks to simulate the point-wise mean radiant temperatures--a driving factor of outdoor human thermal comfort--across various time scales.
  arXiv  Detail & Related papers  (2023-10-09T13:07:23Z)
• Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions [0.0]
  We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.
  arXiv  Detail & Related papers  (2023-04-27T19:53:18Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• A Multi-Scale Deep Learning Framework for Projecting Weather Extremes [3.3598755777055374]
  Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. General circulation models (GCMs), which are currently the primary tool for climate projections, cannot characterize weather extremes accurately. We present a multi-resolution deep-learning framework that corrects a GCM's biases by matching low-order and tail statistics of its output with observations at coarse scales. We use the proposed framework to generate statistically realistic realizations of the climate over Western Europe from a simple GCM corrected using observational atmospheric reanalysis.
  arXiv  Detail & Related papers  (2022-10-21T17:47:05Z)
• The Power of Language: Understanding Sentiment Towards the Climate Emergency using Twitter Data [0.0]
  It could be speculated that there is a relationship between Crude Oil Futures and sentiment towards the Climate Emergency. This study shows that it is possible to split the conversation surrounding the Climate Emergency into 3 distinct topics.
  arXiv  Detail & Related papers  (2021-01-25T19:51:10Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.